Merge branch 'master' of https://github.com/tagteam/riskRegression

Author: Brice Maxime Hugues Ozenne

DESCRIPTION

@@ -2,7 +2,7 @@ Package: riskRegression
 Type: Package
 Title: Risk Regression Models and Prediction Scores for Survival Analysis with
     Competing Risks
-Version: 2026.04.22
+Version: 2026.05.19
 Authors@R: c(person("Thomas Alexander", "Gerds", role = c("aut", "cre"), email = "[email protected]"),
              person("Johan Sebastian", "Ohlendorff", role = "aut"),
              person("Paul", "Blanche", role = "ctb"),

R/ate.R

@@ -3,9 +3,9 @@
 ## author: Thomas Alexander Gerds
 ## created: Oct 23 2016 (08:53) 
 ## Version: 
-## last-updated: Apr 23 2026 (19:28) 
+## last-updated: maj 21 2026 (14:52) 
 ##           By: Brice Ozenne
-##     Update #: 2651
+##     Update #: 2653
 #----------------------------------------------------------------------
 ## 
 ### Commentary: 
@@ -33,7 +33,7 @@
 #' @param data.index [numeric vector] Position of the observation in argument data relative to the dataset used to obtain the argument event, treatment, censor.
 #' Only necessary for the standard errors when computing the Average Treatment Effects on a subset of the data set. 
 #' @param contrasts [character vector] levels of the treatment variable for which the risks should be assessed and compared. Default is to consider all levels.
-#' @param allContrasts [2-row character matrix] levels of the treatment variable to be compared. Default is to consider all pairwise comparisons.
+#' @param allContrasts [2-row character matrix] where each column contains the levels of the treatment variable to be compared. Default is to consider all pairwise comparisons.
 #' @param strata [character] Strata variable on which to compute the average risk.
 #' To be used instead of the treatment argument when the interest lies in the average outcome within group
 #' instead of the average counterfactual outcome had all subjects experience the exposure of a specific group. 

R/autoplot.ate.R

@@ -3,9 +3,9 @@
 ## author: Brice Ozenne
 ## created: apr 28 2017 (14:19) 
 ## Version: 
-## last-updated: apr 27 2026 (18:32) 
+## last-updated: maj 21 2026 (14:52) 
 ##           By: Brice Ozenne
-##     Update #: 269
+##     Update #: 268
 #----------------------------------------------------------------------
 ## 
 ### Commentary: 

R/synthesize.R

@@ -1,11 +1,11 @@
 ### synthesize.R ---
 #----------------------------------------------------------------------
-## Author: Johan Sebastian Ohlendorff Thomas Alexander Gerds
+## Author: Johan Sebastian Ohlendorff & Thomas Alexander Gerds
 ## Created: Apr 28 2021 (09:04)
 ## Version:
-## Last-Updated: feb 12 2026 (12:14) 
-##           By: Thomas Alexander Gerds
-##     Update #: 156
+## Last-Updated: maj 21 2026 (14:54) 
+##           By: Brice Ozenne
+##     Update #: 174
 #----------------------------------------------------------------------
 ##
 ### Commentary:
@@ -248,7 +248,7 @@ synthesize.lvm <- function(object,
                            ...){
   from.formula <- length(attr(object,"from.formula"))>0
   # check whether variables in model are in data set
-  if (!from.formula && !all(object$attributes$eventHistory$time$names %in% names(data))) {
+  if (!from.formula && !all(object$attributes$eventHistory[[1]]$names %in% names(data))) {
     stop("Time or status variable could not be found in data set.")
   }
   var.model <- colnames(object$M)
@@ -507,7 +507,7 @@ synthesize.lvm <- function(object,
   }
   # Estimate regression coefficients in real data
   # and add them to the lvm object using lava::regression
-  for(var in latent_vars[!(latent_vars %in% object$attributes$eventHistory$time$names)]){
+  for(var in latent_vars[!(latent_vars %in% object$attributes$eventHistory[[1]]$names)]){
       covariates <- get_covariates(object,var,dichotomized_variables)
       reg_formula <- as.formula(paste0(var, "~", covariates))
       # we have three types of regression to deal with now. Either